Overview

Dataset statistics

Number of variables35
Number of observations130
Missing cells390
Missing cells (%)8.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.0 KiB
Average record size in memory260.0 B

Variable types

Numeric11
Categorical13
Text2
DateTime2
Unsupported3
Boolean4

Alerts

aborteddataerrorcount has constant value ""Constant
accommodationunavailableerrorcount has constant value ""Constant
createtripformsubmission_directjourney has constant value ""Constant
createtripformsubmission_extrainfo has constant value ""Constant
failureindurationserrorcount has constant value ""Constant
createtripformsubmission_fastjourney has constant value ""Constant
overnightreductionerrorcount has constant value ""Constant
success has constant value ""Constant
transportunavailableerrorcount is highly imbalanced (91.8%)Imbalance
creationdate has 130 (100.0%) missing valuesMissing
jotform_onewaytrip has 130 (100.0%) missing valuesMissing
tripbuildtimeseconds has 130 (100.0%) missing valuesMissing
createtripid_region has unique valuesUnique
creationdate is an unsupported type, check if it needs cleaning or further analysisUnsupported
jotform_onewaytrip is an unsupported type, check if it needs cleaning or further analysisUnsupported
tripbuildtimeseconds is an unsupported type, check if it needs cleaning or further analysisUnsupported
badproportionerrorcount has 87 (66.9%) zerosZeros
firstchoiceaccommodationunavailablecount has 89 (68.5%) zerosZeros
noavailabledotwaccommodationerrorcount has 99 (76.2%) zerosZeros
nofamilymanualfallbackserrorcount has 125 (96.2%) zerosZeros
norouteserrorcount has 46 (35.4%) zerosZeros
substituteaccommodationunavailablecount has 92 (70.8%) zerosZeros

Reproduction

Analysis started2024-02-04 09:22:08.567431
Analysis finished2024-02-04 09:22:20.706582
Duration12.14 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

createtripid
Real number (ℝ)

Distinct100
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6156834 × 1014
Minimum6.0285046 × 1014
Maximum9.1738366 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:20.774928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6.0285046 × 1014
5-th percentile6.1312692 × 1014
Q16.7532768 × 1014
median7.6457361 × 1014
Q38.6002908 × 1014
95-th percentile8.9916719 × 1014
Maximum9.1738366 × 1014
Range3.1453319 × 1014
Interquartile range (IQR)1.847014 × 1014

Descriptive statistics

Standard deviation9.7211611 × 1013
Coefficient of variation (CV)0.1276466
Kurtosis-1.3831741
Mean7.6156834 × 1014
Median Absolute Deviation (MAD)9.3034815 × 1013
Skewness-0.016126638
Sum9.9003884 × 1016
Variance9.4500973 × 1027
MonotonicityNot monotonic
2024-02-04T09:22:20.908134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.129865837 × 10143
 
2.3%
8.878448985 × 10143
 
2.3%
9.148222706 × 10143
 
2.3%
8.603450218 × 10143
 
2.3%
6.325318859 × 10143
 
2.3%
8.699519905 × 10143
 
2.3%
7.743813566 × 10142
 
1.5%
8.645674085 × 10142
 
1.5%
8.492562173 × 10142
 
1.5%
8.847932007 × 10142
 
1.5%
Other values (90) 104
80.0%
ValueCountFrequency (%)
6.028504642 × 10141
 
0.8%
6.090115823 × 10141
 
0.8%
6.106867344 × 10141
 
0.8%
6.129865837 × 10143
2.3%
6.131269209 × 10142
1.5%
6.194806186 × 10141
 
0.8%
6.238749657 × 10142
1.5%
6.247531057 × 10141
 
0.8%
6.28563991 × 10141
 
0.8%
6.292823681 × 10141
 
0.8%
ValueCountFrequency (%)
9.173836571 × 10141
 
0.8%
9.148222706 × 10143
2.3%
9.140777644 × 10141
 
0.8%
9.118122609 × 10141
 
0.8%
9.024857658 × 10141
 
0.8%
8.951111524 × 10142
1.5%
8.894085324 × 10141
 
0.8%
8.878448985 × 10143
2.3%
8.856509338 × 10141
 
0.8%
8.847932007 × 10142
1.5%

region
Categorical

Distinct18
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Yorkshire
42 
Highlands & Islands
29 
East Highlands
20 
Andalusia
French Riviera
Other values (13)
29 

Length

Max length21
Median length19
Mean length12.653846
Min length6

Characters and Unicode

Total characters1645
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)4.6%

Sample

1st rowHighlands & Islands
2nd rowYorkshire
3rd rowYorkshire
4th rowEast Highlands
5th rowAndalusia

Common Values

ValueCountFrequency (%)
Yorkshire 42
32.3%
Highlands & Islands 29
22.3%
East Highlands 20
15.4%
Andalusia 5
 
3.8%
French Riviera 5
 
3.8%
Catalonia 5
 
3.8%
Alsace 4
 
3.1%
Hauts-de-France 4
 
3.1%
Northern Italy 3
 
2.3%
Corsica 3
 
2.3%
Other values (8) 10
 
7.7%

Length

2024-02-04T09:22:21.041086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
highlands 49
21.8%
yorkshire 42
18.7%
31
13.8%
islands 29
12.9%
east 20
8.9%
riviera 6
 
2.7%
andalusia 5
 
2.2%
french 5
 
2.2%
catalonia 5
 
2.2%
hauts-de-france 4
 
1.8%
Other values (15) 29
12.9%

Most occurring characters

ValueCountFrequency (%)
s 186
 
11.3%
a 159
 
9.7%
i 121
 
7.4%
r 118
 
7.2%
n 110
 
6.7%
l 106
 
6.4%
h 104
 
6.3%
95
 
5.8%
d 89
 
5.4%
e 76
 
4.6%
Other values (24) 481
29.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1313
79.8%
Uppercase Letter 198
 
12.0%
Space Separator 95
 
5.8%
Other Punctuation 31
 
1.9%
Dash Punctuation 8
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 186
14.2%
a 159
12.1%
i 121
9.2%
r 118
9.0%
n 110
8.4%
l 106
8.1%
h 104
7.9%
d 89
6.8%
e 76
5.8%
o 59
 
4.5%
Other values (11) 185
14.1%
Uppercase Letter
ValueCountFrequency (%)
H 53
26.8%
Y 42
21.2%
I 34
17.2%
E 20
 
10.1%
C 13
 
6.6%
A 9
 
4.5%
F 9
 
4.5%
R 7
 
3.5%
N 7
 
3.5%
S 4
 
2.0%
Space Separator
ValueCountFrequency (%)
95
100.0%
Other Punctuation
ValueCountFrequency (%)
& 31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1511
91.9%
Common 134
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 186
12.3%
a 159
10.5%
i 121
 
8.0%
r 118
 
7.8%
n 110
 
7.3%
l 106
 
7.0%
h 104
 
6.9%
d 89
 
5.9%
e 76
 
5.0%
o 59
 
3.9%
Other values (21) 383
25.3%
Common
ValueCountFrequency (%)
95
70.9%
& 31
 
23.1%
- 8
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 186
 
11.3%
a 159
 
9.7%
i 121
 
7.4%
r 118
 
7.2%
n 110
 
6.7%
l 106
 
6.4%
h 104
 
6.3%
95
 
5.8%
d 89
 
5.4%
e 76
 
4.6%
Other values (24) 481
29.2%

createtripid_region
Text

UNIQUE 

Distinct130
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:21.182232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length37
Median length35
Mean length28.653846
Min length22

Characters and Unicode

Total characters3725
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)100.0%

Sample

1st row856446659120383_Highlands & Islands
2nd row675305430969085_Yorkshire
3rd row714914505173585_Yorkshire
4th row619480618589444_East Highlands
5th row687603529986566_Andalusia
ValueCountFrequency (%)
31
 
13.8%
islands 29
 
12.9%
highlands 20
 
8.9%
riviera 6
 
2.7%
italy 4
 
1.8%
spain 2
 
0.9%
central 2
 
0.9%
808561449369965_yorkshire 1
 
0.4%
764573606047540_east 1
 
0.4%
727428801083979_yorkshire 1
 
0.4%
Other values (128) 128
56.9%
2024-02-04T09:22:21.452278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 238
 
6.4%
8 232
 
6.2%
4 203
 
5.4%
5 202
 
5.4%
2 189
 
5.1%
7 187
 
5.0%
s 186
 
5.0%
9 184
 
4.9%
0 175
 
4.7%
1 171
 
4.6%
Other values (35) 1758
47.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
52.3%
Lowercase Letter 1313
35.2%
Uppercase Letter 198
 
5.3%
Connector Punctuation 130
 
3.5%
Space Separator 95
 
2.6%
Other Punctuation 31
 
0.8%
Dash Punctuation 8
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 186
14.2%
a 159
12.1%
i 121
9.2%
r 118
9.0%
n 110
8.4%
l 106
8.1%
h 104
7.9%
d 89
6.8%
e 76
5.8%
o 59
 
4.5%
Other values (11) 185
14.1%
Decimal Number
ValueCountFrequency (%)
6 238
12.2%
8 232
11.9%
4 203
10.4%
5 202
10.4%
2 189
9.7%
7 187
9.6%
9 184
9.4%
0 175
9.0%
1 171
8.8%
3 169
8.7%
Uppercase Letter
ValueCountFrequency (%)
H 53
26.8%
Y 42
21.2%
I 34
17.2%
E 20
 
10.1%
C 13
 
6.6%
F 9
 
4.5%
A 9
 
4.5%
R 7
 
3.5%
N 7
 
3.5%
S 4
 
2.0%
Connector Punctuation
ValueCountFrequency (%)
_ 130
100.0%
Space Separator
ValueCountFrequency (%)
95
100.0%
Other Punctuation
ValueCountFrequency (%)
& 31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2214
59.4%
Latin 1511
40.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 186
12.3%
a 159
10.5%
i 121
 
8.0%
r 118
 
7.8%
n 110
 
7.3%
l 106
 
7.0%
h 104
 
6.9%
d 89
 
5.9%
e 76
 
5.0%
o 59
 
3.9%
Other values (21) 383
25.3%
Common
ValueCountFrequency (%)
6 238
10.7%
8 232
10.5%
4 203
9.2%
5 202
9.1%
2 189
8.5%
7 187
8.4%
9 184
8.3%
0 175
7.9%
1 171
7.7%
3 169
7.6%
Other values (4) 264
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 238
 
6.4%
8 232
 
6.2%
4 203
 
5.4%
5 202
 
5.4%
2 189
 
5.1%
7 187
 
5.0%
s 186
 
5.0%
9 184
 
4.9%
0 175
 
4.7%
1 171
 
4.6%
Other values (35) 1758
47.2%
Distinct100
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum2024-01-20 10:45:24.467000
Maximum2024-01-24 22:41:30.125000
2024-02-04T09:22:21.577757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:21.715639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

aborteddataerrorcount
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
130 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 130
100.0%

Length

2024-02-04T09:22:21.844657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:21.933084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 130
100.0%

Most occurring characters

ValueCountFrequency (%)
0 130
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
100.0%
Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
130 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 130
100.0%

Length

2024-02-04T09:22:22.021330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:22.105004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 130
100.0%

Most occurring characters

ValueCountFrequency (%)
0 130
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
100.0%
Distinct6
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2076923
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:22.184005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4.55
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0543094
Coefficient of variation (CV)0.47756174
Kurtosis3.4965675
Mean2.2076923
Median Absolute Deviation (MAD)0
Skewness1.7499756
Sum287
Variance1.1115683
MonotonicityNot monotonic
2024-02-04T09:22:22.276968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 82
63.1%
1 23
 
17.7%
3 10
 
7.7%
4 8
 
6.2%
5 4
 
3.1%
6 3
 
2.3%
ValueCountFrequency (%)
1 23
 
17.7%
2 82
63.1%
3 10
 
7.7%
4 8
 
6.2%
5 4
 
3.1%
6 3
 
2.3%
ValueCountFrequency (%)
6 3
 
2.3%
5 4
 
3.1%
4 8
 
6.2%
3 10
 
7.7%
2 82
63.1%
1 23
 
17.7%

badproportionerrorcount
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9
Minimum0
Maximum88
Zeros87
Zeros (%)66.9%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:22.379689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311.75
95-th percentile43.55
Maximum88
Range88
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation18.534694
Coefficient of variation (CV)2.0594105
Kurtosis7.1677848
Mean9
Median Absolute Deviation (MAD)0
Skewness2.6172715
Sum1170
Variance343.53488
MonotonicityNot monotonic
2024-02-04T09:22:22.489289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 87
66.9%
14 4
 
3.1%
2 4
 
3.1%
40 3
 
2.3%
37 2
 
1.5%
48 2
 
1.5%
28 2
 
1.5%
13 2
 
1.5%
18 2
 
1.5%
30 2
 
1.5%
Other values (17) 20
 
15.4%
ValueCountFrequency (%)
0 87
66.9%
1 1
 
0.8%
2 4
 
3.1%
3 1
 
0.8%
4 1
 
0.8%
6 1
 
0.8%
10 1
 
0.8%
11 1
 
0.8%
12 1
 
0.8%
13 2
 
1.5%
ValueCountFrequency (%)
88 1
 
0.8%
86 2
1.5%
80 1
 
0.8%
48 2
1.5%
44 1
 
0.8%
43 1
 
0.8%
42 1
 
0.8%
40 3
2.3%
37 2
1.5%
30 2
1.5%
Distinct20
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
{}
84 
{13}
 
6
{15}
 
5
{14}
 
5
{9,13}
 
4
Other values (15)
26 

Length

Max length12
Median length2
Mean length3.3846154
Min length2

Characters and Unicode

Total characters440
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)5.4%

Sample

1st row{}
2nd row{7}
3rd row{}
4th row{}
5th row{}

Common Values

ValueCountFrequency (%)
{} 84
64.6%
{13} 6
 
4.6%
{15} 5
 
3.8%
{14} 5
 
3.8%
{9,13} 4
 
3.1%
{7,5} 3
 
2.3%
{12,12,15} 3
 
2.3%
{12,8} 3
 
2.3%
{13,10,10,7} 2
 
1.5%
{11,13} 2
 
1.5%
Other values (10) 13
 
10.0%

Length

2024-02-04T09:22:22.599088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
84
64.6%
13 6
 
4.6%
15 5
 
3.8%
14 5
 
3.8%
9,13 4
 
3.1%
7,5 3
 
2.3%
12,12,15 3
 
2.3%
12,8 3
 
2.3%
7,8 2
 
1.5%
14,13 2
 
1.5%
Other values (10) 13
 
10.0%

Most occurring characters

ValueCountFrequency (%)
{ 130
29.5%
} 130
29.5%
1 59
13.4%
, 39
 
8.9%
3 18
 
4.1%
5 17
 
3.9%
7 11
 
2.5%
2 9
 
2.0%
9 8
 
1.8%
4 7
 
1.6%
Other values (3) 12
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141
32.0%
Open Punctuation 130
29.5%
Close Punctuation 130
29.5%
Other Punctuation 39
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 59
41.8%
3 18
 
12.8%
5 17
 
12.1%
7 11
 
7.8%
2 9
 
6.4%
9 8
 
5.7%
4 7
 
5.0%
8 6
 
4.3%
0 5
 
3.5%
6 1
 
0.7%
Open Punctuation
ValueCountFrequency (%)
{ 130
100.0%
Close Punctuation
ValueCountFrequency (%)
} 130
100.0%
Other Punctuation
ValueCountFrequency (%)
, 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
{ 130
29.5%
} 130
29.5%
1 59
13.4%
, 39
 
8.9%
3 18
 
4.1%
5 17
 
3.9%
7 11
 
2.5%
2 9
 
2.0%
9 8
 
1.8%
4 7
 
1.6%
Other values (3) 12
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
{ 130
29.5%
} 130
29.5%
1 59
13.4%
, 39
 
8.9%
3 18
 
4.1%
5 17
 
3.9%
7 11
 
2.5%
2 9
 
2.0%
9 8
 
1.8%
4 7
 
1.6%
Other values (3) 12
 
2.7%
Distinct5
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
84 
1
19 
2
17 
3
 
8
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 84
64.6%
1 19
 
14.6%
2 17
 
13.1%
3 8
 
6.2%
4 2
 
1.5%

Length

2024-02-04T09:22:22.916440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:23.011065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 84
64.6%
1 19
 
14.6%
2 17
 
13.1%
3 8
 
6.2%
4 2
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 84
64.6%
1 19
 
14.6%
2 17
 
13.1%
3 8
 
6.2%
4 2
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84
64.6%
1 19
 
14.6%
2 17
 
13.1%
3 8
 
6.2%
4 2
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 84
64.6%
1 19
 
14.6%
2 17
 
13.1%
3 8
 
6.2%
4 2
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84
64.6%
1 19
 
14.6%
2 17
 
13.1%
3 8
 
6.2%
4 2
 
1.5%
Distinct20
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87791889
Minimum26
Maximum8.3489087 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:23.116028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile26
Q126
median26
Q322387470
95-th percentile6.357787 × 108
Maximum8.3489087 × 108
Range8.3489084 × 108
Interquartile range (IQR)22387444

Descriptive statistics

Standard deviation2.0329636 × 108
Coefficient of variation (CV)2.3156622
Kurtosis7.5007007
Mean87791889
Median Absolute Deviation (MAD)0
Skewness2.8385321
Sum1.1412946 × 1010
Variance4.1329409 × 1016
MonotonicityNot monotonic
2024-02-04T09:22:23.223215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
26 70
53.8%
29 7
 
5.4%
156763922 6
 
4.6%
22376242 6
 
4.6%
833158856 5
 
3.8%
34 5
 
3.8%
394536297 4
 
3.1%
3117 4
 
3.1%
183936590 4
 
3.1%
306452460 4
 
3.1%
Other values (10) 15
 
11.5%
ValueCountFrequency (%)
26 70
53.8%
29 7
 
5.4%
34 5
 
3.8%
3117 4
 
3.1%
22371672 1
 
0.8%
22371813 2
 
1.5%
22371835 1
 
0.8%
22371862 1
 
0.8%
22376242 6
 
4.6%
22391212 1
 
0.8%
ValueCountFrequency (%)
834890871 1
 
0.8%
834890865 1
 
0.8%
833158856 5
3.8%
394536297 4
3.1%
375285198 1
 
0.8%
306452460 4
3.1%
183936590 4
3.1%
156763922 6
4.6%
108321288 3
2.3%
42771065 3
2.3%
Distinct20
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
London
70 
Bristol
 
7
Cambridge
 
6
Brighton
 
6
Coventry
 
5
Other values (15)
36 

Length

Max length17
Median length6
Mean length7.0461538
Min length4

Characters and Unicode

Total characters916
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)5.4%

Sample

1st rowCoventry
2nd rowLondon
3rd rowLondon
4th rowChelmsford
5th rowLondon

Common Values

ValueCountFrequency (%)
London 70
53.8%
Bristol 7
 
5.4%
Cambridge 6
 
4.6%
Brighton 6
 
4.6%
Coventry 5
 
3.8%
Manchester 5
 
3.8%
Guildford 4
 
3.1%
Bath 4
 
3.1%
Cardiff 4
 
3.1%
Chelmsford 4
 
3.1%
Other values (10) 15
 
11.5%

Length

2024-02-04T09:22:23.340744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
london 70
52.2%
bristol 7
 
5.2%
cambridge 6
 
4.5%
brighton 6
 
4.5%
coventry 5
 
3.7%
manchester 5
 
3.7%
guildford 4
 
3.0%
bath 4
 
3.0%
cardiff 4
 
3.0%
chelmsford 4
 
3.0%
Other values (12) 19
 
14.2%

Most occurring characters

ValueCountFrequency (%)
o 176
19.2%
n 159
17.4%
d 99
10.8%
L 76
8.3%
r 53
 
5.8%
e 41
 
4.5%
i 36
 
3.9%
t 35
 
3.8%
h 27
 
2.9%
s 25
 
2.7%
Other values (23) 189
20.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 776
84.7%
Uppercase Letter 134
 
14.6%
Space Separator 4
 
0.4%
Other Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 176
22.7%
n 159
20.5%
d 99
12.8%
r 53
 
6.8%
e 41
 
5.3%
i 36
 
4.6%
t 35
 
4.5%
h 27
 
3.5%
s 25
 
3.2%
a 23
 
3.0%
Other values (11) 102
13.1%
Uppercase Letter
ValueCountFrequency (%)
L 76
56.7%
C 21
 
15.7%
B 17
 
12.7%
M 5
 
3.7%
G 4
 
3.0%
A 3
 
2.2%
E 3
 
2.2%
S 2
 
1.5%
D 2
 
1.5%
P 1
 
0.7%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 910
99.3%
Common 6
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 176
19.3%
n 159
17.5%
d 99
10.9%
L 76
8.4%
r 53
 
5.8%
e 41
 
4.5%
i 36
 
4.0%
t 35
 
3.8%
h 27
 
3.0%
s 25
 
2.7%
Other values (21) 183
20.1%
Common
ValueCountFrequency (%)
4
66.7%
' 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 176
19.2%
n 159
17.4%
d 99
10.8%
L 76
8.3%
r 53
 
5.8%
e 41
 
4.5%
i 36
 
3.9%
t 35
 
3.8%
h 27
 
2.9%
s 25
 
2.7%
Other values (23) 189
20.6%

creationdate
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing130
Missing (%)100.0%
Memory size2.0 KiB
Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
130 
ValueCountFrequency (%)
False 130
100.0%
2024-02-04T09:22:23.433741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct12
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2615385
Minimum3
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:23.513106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median7
Q37
95-th percentile14
Maximum25
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5732725
Coefficient of variation (CV)0.57067006
Kurtosis7.0734293
Mean6.2615385
Median Absolute Deviation (MAD)2
Skewness2.1037604
Sum814
Variance12.768277
MonotonicityNot monotonic
2024-02-04T09:22:23.608813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 52
40.0%
3 41
31.5%
5 8
 
6.2%
4 7
 
5.4%
14 7
 
5.4%
10 5
 
3.8%
6 4
 
3.1%
12 2
 
1.5%
9 1
 
0.8%
25 1
 
0.8%
Other values (2) 2
 
1.5%
ValueCountFrequency (%)
3 41
31.5%
4 7
 
5.4%
5 8
 
6.2%
6 4
 
3.1%
7 52
40.0%
8 1
 
0.8%
9 1
 
0.8%
10 5
 
3.8%
12 2
 
1.5%
14 7
 
5.4%
ValueCountFrequency (%)
25 1
 
0.8%
21 1
 
0.8%
14 7
 
5.4%
12 2
 
1.5%
10 5
 
3.8%
9 1
 
0.8%
8 1
 
0.8%
7 52
40.0%
6 4
 
3.1%
5 8
 
6.2%
Distinct76
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:23.725696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length81
Median length52
Mean length35.2
Min length3

Characters and Unicode

Total characters4576
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)33.1%

Sample

1st rowRelaxation,History
2nd rowNature,Good for kids
3rd rowNature,Food and Drink,History,Arts and Culture
4th rowHistory,Food and Drink,Nature
5th rowArts and Culture,History,Food and Drink,Relaxation,Nature
ValueCountFrequency (%)
and 103
25.1%
for 37
 
9.0%
good 21
 
5.1%
drink,nature 19
 
4.6%
arts 15
 
3.7%
drink 14
 
3.4%
kids 14
 
3.4%
relaxation,food 10
 
2.4%
relaxation,history,food 10
 
2.4%
culture 9
 
2.2%
Other values (75) 158
38.5%
2024-02-04T09:22:24.021811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 398
 
8.7%
r 394
 
8.6%
t 364
 
8.0%
a 344
 
7.5%
, 308
 
6.7%
n 290
 
6.3%
e 289
 
6.3%
d 286
 
6.2%
280
 
6.1%
i 266
 
5.8%
Other values (18) 1357
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3451
75.4%
Uppercase Letter 537
 
11.7%
Other Punctuation 308
 
6.7%
Space Separator 280
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 398
11.5%
r 394
11.4%
t 364
10.5%
a 344
10.0%
n 290
8.4%
e 289
8.4%
d 286
8.3%
i 266
7.7%
u 208
6.0%
s 153
 
4.4%
Other values (8) 459
13.3%
Uppercase Letter
ValueCountFrequency (%)
N 102
19.0%
H 81
15.1%
A 76
14.2%
R 70
13.0%
D 68
12.7%
F 68
12.7%
G 37
 
6.9%
C 35
 
6.5%
Other Punctuation
ValueCountFrequency (%)
, 308
100.0%
Space Separator
ValueCountFrequency (%)
280
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3988
87.2%
Common 588
 
12.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 398
 
10.0%
r 394
 
9.9%
t 364
 
9.1%
a 344
 
8.6%
n 290
 
7.3%
e 289
 
7.2%
d 286
 
7.2%
i 266
 
6.7%
u 208
 
5.2%
s 153
 
3.8%
Other values (16) 996
25.0%
Common
ValueCountFrequency (%)
, 308
52.4%
280
47.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 398
 
8.7%
r 394
 
8.6%
t 364
 
8.0%
a 344
 
7.5%
, 308
 
6.7%
n 290
 
6.3%
e 289
 
6.3%
d 286
 
6.2%
280
 
6.1%
i 266
 
5.8%
Other values (18) 1357
29.7%
Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
{}
130 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters260
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row{}
2nd row{}
3rd row{}
4th row{}
5th row{}

Common Values

ValueCountFrequency (%)
{} 130
100.0%

Length

2024-02-04T09:22:24.140404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:24.226104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
130
100.0%

Most occurring characters

ValueCountFrequency (%)
{ 130
50.0%
} 130
50.0%

Most occurring categories

ValueCountFrequency (%)
Open Punctuation 130
50.0%
Close Punctuation 130
50.0%

Most frequent character per category

Open Punctuation
ValueCountFrequency (%)
{ 130
100.0%
Close Punctuation
ValueCountFrequency (%)
} 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 260
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
{ 130
50.0%
} 130
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
{ 130
50.0%
} 130
50.0%

failtimeseconds
Real number (ℝ)

Distinct127
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean593.91354
Minimum2.65
Maximum28218.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:24.322264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.65
5-th percentile4.2635
Q17.4425
median27.725
Q361.03
95-th percentile824.216
Maximum28218.37
Range28215.72
Interquartile range (IQR)53.5875

Descriptive statistics

Standard deviation3195.0613
Coefficient of variation (CV)5.3796741
Kurtosis53.573633
Mean593.91354
Median Absolute Deviation (MAD)21.27
Skewness7.1194547
Sum77208.76
Variance10208416
MonotonicityNot monotonic
2024-02-04T09:22:24.448664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.2 2
 
1.5%
30.78 2
 
1.5%
6.83 2
 
1.5%
11.65 1
 
0.8%
5.32 1
 
0.8%
45.31 1
 
0.8%
21.65 1
 
0.8%
48.94 1
 
0.8%
2.87 1
 
0.8%
38.69 1
 
0.8%
Other values (117) 117
90.0%
ValueCountFrequency (%)
2.65 1
0.8%
2.87 1
0.8%
2.91 1
0.8%
3.26 1
0.8%
3.56 1
0.8%
3.93 1
0.8%
4.25 1
0.8%
4.28 1
0.8%
4.6 1
0.8%
4.64 1
0.8%
ValueCountFrequency (%)
28218.37 1
0.8%
19266.32 1
0.8%
12860.31 1
0.8%
4860.36 1
0.8%
1260.54 1
0.8%
1041.07 1
0.8%
932.72 1
0.8%
691.6 1
0.8%
549.48 1
0.8%
542.18 1
0.8%

failureindurationserrorcount
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
130 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 130
100.0%

Length

2024-02-04T09:22:24.566219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:24.649588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 130
100.0%

Most occurring characters

ValueCountFrequency (%)
0 130
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
100.0%
Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
130 
ValueCountFrequency (%)
False 130
100.0%
2024-02-04T09:22:24.721621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct29
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.184615
Minimum0
Maximum396
Zeros89
Zeros (%)68.5%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:24.810993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile217.9
Maximum396
Range396
Interquartile range (IQR)1

Descriptive statistics

Standard deviation76.138774
Coefficient of variation (CV)2.9077675
Kurtosis10.556768
Mean26.184615
Median Absolute Deviation (MAD)0
Skewness3.3405439
Sum3404
Variance5797.1129
MonotonicityNot monotonic
2024-02-04T09:22:24.930528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 89
68.5%
1 9
 
6.9%
25 3
 
2.3%
18 2
 
1.5%
338 2
 
1.5%
12 2
 
1.5%
44 1
 
0.8%
15 1
 
0.8%
294 1
 
0.8%
80 1
 
0.8%
Other values (19) 19
 
14.6%
ValueCountFrequency (%)
0 89
68.5%
1 9
 
6.9%
2 1
 
0.8%
4 1
 
0.8%
5 1
 
0.8%
6 1
 
0.8%
9 1
 
0.8%
10 1
 
0.8%
11 1
 
0.8%
12 2
 
1.5%
ValueCountFrequency (%)
396 1
0.8%
338 2
1.5%
314 1
0.8%
294 1
0.8%
258 1
0.8%
226 1
0.8%
208 1
0.8%
182 1
0.8%
170 1
0.8%
122 1
0.8%
Distinct25
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.746154
Minimum0
Maximum1118
Zeros99
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:25.045994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile694.75
Maximum1118
Range1118
Interquartile range (IQR)0

Descriptive statistics

Standard deviation220.63679
Coefficient of variation (CV)2.9518147
Kurtosis9.2028072
Mean74.746154
Median Absolute Deviation (MAD)0
Skewness3.1820798
Sum9717
Variance48680.594
MonotonicityNot monotonic
2024-02-04T09:22:25.157568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 99
76.2%
1 6
 
4.6%
48 2
 
1.5%
55 2
 
1.5%
110 1
 
0.8%
28 1
 
0.8%
792 1
 
0.8%
348 1
 
0.8%
98 1
 
0.8%
811 1
 
0.8%
Other values (15) 15
 
11.5%
ValueCountFrequency (%)
0 99
76.2%
1 6
 
4.6%
2 1
 
0.8%
12 1
 
0.8%
28 1
 
0.8%
48 2
 
1.5%
55 2
 
1.5%
56 1
 
0.8%
98 1
 
0.8%
110 1
 
0.8%
ValueCountFrequency (%)
1118 1
0.8%
920 1
0.8%
919 1
0.8%
846 1
0.8%
811 1
0.8%
792 1
0.8%
706 1
0.8%
681 1
0.8%
622 1
0.8%
581 1
0.8%

nofamilymanualfallbackserrorcount
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5076923
Minimum0
Maximum62
Zeros125
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:25.248024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.2558376
Coefficient of variation (CV)5.4758107
Kurtosis35.074198
Mean1.5076923
Median Absolute Deviation (MAD)0
Skewness5.8626267
Sum196
Variance68.158855
MonotonicityNot monotonic
2024-02-04T09:22:25.338453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 125
96.2%
62 1
 
0.8%
38 1
 
0.8%
50 1
 
0.8%
12 1
 
0.8%
34 1
 
0.8%
ValueCountFrequency (%)
0 125
96.2%
12 1
 
0.8%
34 1
 
0.8%
38 1
 
0.8%
50 1
 
0.8%
62 1
 
0.8%
ValueCountFrequency (%)
62 1
 
0.8%
50 1
 
0.8%
38 1
 
0.8%
34 1
 
0.8%
12 1
 
0.8%
0 125
96.2%
Distinct4
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
73 
1
38 
2
18 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 73
56.2%
1 38
29.2%
2 18
 
13.8%
4 1
 
0.8%

Length

2024-02-04T09:22:25.441290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:25.532236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 73
56.2%
1 38
29.2%
2 18
 
13.8%
4 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 73
56.2%
1 38
29.2%
2 18
 
13.8%
4 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73
56.2%
1 38
29.2%
2 18
 
13.8%
4 1
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73
56.2%
1 38
29.2%
2 18
 
13.8%
4 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73
56.2%
1 38
29.2%
2 18
 
13.8%
4 1
 
0.8%

norouteserrorcount
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0615385
Minimum0
Maximum134
Zeros46
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:25.630476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38.75
95-th percentile41.1
Maximum134
Range134
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation21.448967
Coefficient of variation (CV)2.3670337
Kurtosis17.411728
Mean9.0615385
Median Absolute Deviation (MAD)2
Skewness4.0250347
Sum1178
Variance460.0582
MonotonicityNot monotonic
2024-02-04T09:22:25.745508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 46
35.4%
1 14
 
10.8%
2 13
 
10.0%
9 9
 
6.9%
3 7
 
5.4%
14 5
 
3.8%
4 5
 
3.8%
7 4
 
3.1%
6 4
 
3.1%
10 3
 
2.3%
Other values (16) 20
15.4%
ValueCountFrequency (%)
0 46
35.4%
1 14
 
10.8%
2 13
 
10.0%
3 7
 
5.4%
4 5
 
3.8%
5 2
 
1.5%
6 4
 
3.1%
7 4
 
3.1%
8 2
 
1.5%
9 9
 
6.9%
ValueCountFrequency (%)
134 1
0.8%
120 1
0.8%
100 1
0.8%
93 1
0.8%
65 1
0.8%
60 1
0.8%
42 1
0.8%
40 1
0.8%
38 1
0.8%
36 1
0.8%

jotform_onewaytrip
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing130
Missing (%)100.0%
Memory size2.0 KiB

overnightreductionerrorcount
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
130 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 130
100.0%

Length

2024-02-04T09:22:25.855736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:25.939205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 130
100.0%

Most occurring characters

ValueCountFrequency (%)
0 130
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
100.0%
Distinct66
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum2024-02-10 00:00:00
Maximum2025-10-29 00:00:00
2024-02-04T09:22:26.043235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:26.186127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct5
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1
94 
3
16 
2
15 
6
 
3
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 94
72.3%
3 16
 
12.3%
2 15
 
11.5%
6 3
 
2.3%
4 2
 
1.5%

Length

2024-02-04T09:22:26.304468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:26.399953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 94
72.3%
3 16
 
12.3%
2 15
 
11.5%
6 3
 
2.3%
4 2
 
1.5%

Most occurring characters

ValueCountFrequency (%)
1 94
72.3%
3 16
 
12.3%
2 15
 
11.5%
6 3
 
2.3%
4 2
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 94
72.3%
3 16
 
12.3%
2 15
 
11.5%
6 3
 
2.3%
4 2
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 94
72.3%
3 16
 
12.3%
2 15
 
11.5%
6 3
 
2.3%
4 2
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 94
72.3%
3 16
 
12.3%
2 15
 
11.5%
6 3
 
2.3%
4 2
 
1.5%
Distinct29
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.161538
Minimum0
Maximum396
Zeros92
Zeros (%)70.8%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-02-04T09:22:26.507214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile217.9
Maximum396
Range396
Interquartile range (IQR)1

Descriptive statistics

Standard deviation76.146615
Coefficient of variation (CV)2.9106322
Kurtosis10.555381
Mean26.161538
Median Absolute Deviation (MAD)0
Skewness3.3403295
Sum3401
Variance5798.307
MonotonicityNot monotonic
2024-02-04T09:22:26.629547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 92
70.8%
1 6
 
4.6%
25 3
 
2.3%
18 2
 
1.5%
338 2
 
1.5%
12 2
 
1.5%
44 1
 
0.8%
15 1
 
0.8%
294 1
 
0.8%
80 1
 
0.8%
Other values (19) 19
 
14.6%
ValueCountFrequency (%)
0 92
70.8%
1 6
 
4.6%
2 1
 
0.8%
4 1
 
0.8%
5 1
 
0.8%
6 1
 
0.8%
9 1
 
0.8%
10 1
 
0.8%
11 1
 
0.8%
12 2
 
1.5%
ValueCountFrequency (%)
396 1
0.8%
338 2
1.5%
314 1
0.8%
294 1
0.8%
258 1
0.8%
226 1
0.8%
208 1
0.8%
182 1
0.8%
170 1
0.8%
122 1
0.8%

success
Boolean

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
130 
ValueCountFrequency (%)
False 130
100.0%
2024-02-04T09:22:26.919737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
87 
1
33 
2
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 87
66.9%
1 33
 
25.4%
2 10
 
7.7%

Length

2024-02-04T09:22:27.005529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:27.095522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 87
66.9%
1 33
 
25.4%
2 10
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 87
66.9%
1 33
 
25.4%
2 10
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 87
66.9%
1 33
 
25.4%
2 10
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 87
66.9%
1 33
 
25.4%
2 10
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 87
66.9%
1 33
 
25.4%
2 10
 
7.7%

transportunavailableerrorcount
Categorical

IMBALANCE 

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
128 
2
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 128
98.5%
2 1
 
0.8%
1 1
 
0.8%

Length

2024-02-04T09:22:27.193090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:22:27.283123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 128
98.5%
2 1
 
0.8%
1 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 128
98.5%
2 1
 
0.8%
1 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 128
98.5%
2 1
 
0.8%
1 1
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 128
98.5%
2 1
 
0.8%
1 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 128
98.5%
2 1
 
0.8%
1 1
 
0.8%

tripbuildtimeseconds
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing130
Missing (%)100.0%
Memory size2.0 KiB
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
114 
True
16 
ValueCountFrequency (%)
False 114
87.7%
True 16
 
12.3%
2024-02-04T09:22:27.365581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Interactions

2024-02-04T09:22:19.086763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:08.961081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.839473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.901225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.729321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.583064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.449315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:14.742621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:15.948685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.865813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:17.940532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:19.173759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.041364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.086708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.981332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.808583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.661522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.528227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:14.843536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.031013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.960020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:18.038577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:19.257545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.120838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.161510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.055381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.882490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.736438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.606093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:14.936143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.106401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:17.049007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:18.131077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:19.334539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.198271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.233438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.124002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.954913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.812177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.678101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:15.052187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.181474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:17.139542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:18.218610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:19.413740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.273184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.309579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.195065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.025306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.891044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.754723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:15.191432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.260236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:17.233457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:18.323384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:19.490760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.347546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.381845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.264457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.098757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.967935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.830858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:15.338778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.340081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:17.333837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:18.401566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:19.572969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.424094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.458175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.337838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.174578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.043437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.909959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:15.449333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.421351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:17.433068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:18.483513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:19.663737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.512121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.545345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.419711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.260777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.129010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:14.047052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:15.553303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.512208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:17.548899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:18.577021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:19.744757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.588856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.646927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.493579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.335090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.203043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:14.178938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:15.646732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.593174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:17.644170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:18.657246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:19.829890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.669561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.729380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.568715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.415465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.283118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:14.476713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:15.752406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.680667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:17.741166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:18.740003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:19.916286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:09.751551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:10.811662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:11.646946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:12.496518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:13.362897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:14.618878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:15.849482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:16.771973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:17.838583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-04T09:22:18.996939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-02-04T09:22:20.091467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-04T09:22:20.530856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

createtripidregioncreatetripid_regionrequestdateaborteddataerrorcountaccommodationunavailableerrorcountcreatetripformsubmission_adultcountbadproportionerrorcountcreatetripformsubmission_childagescreatetripformsubmission_childcountcreatetripformsubmission_closestcity_idcreatetripformsubmission_closestcitycreationdatecreatetripformsubmission_directjourneycreatetripformsubmission_durationdaysarray_to_stringcreatetripformsubmission_extrainfofailtimesecondsfailureindurationserrorcountcreatetripformsubmission_fastjourneyfirstchoiceaccommodationunavailablecountnoavailabledotwaccommodationerrorcountnofamilymanualfallbackserrorcountnogoodscoreerrorcountnorouteserrorcountjotform_onewaytripovernightreductionerrorcountcreatetripformsubmission_preferreddatecreatetripformsubmission_roomcountsubstituteaccommodationunavailablecountsuccesstimeouterrorcounttransportunavailableerrorcounttripbuildtimesecondscreatetripformsubmission_twinroompreferred
2856446659120383Highlands & Islands856446659120383_Highlands & Islands2024-01-20 10:45:24.46700214{}0833158856CoventryNaTFalse3Relaxation,History{}11.650False11019NaN02024-05-1311False00NaNFalse
21675305430969085Yorkshire675305430969085_Yorkshire2024-01-20 11:14:28.2180020{7}126LondonNaTFalse4Nature,Good for kids{}19266.320False00002NaN02024-03-3010False00NaNFalse
36714914505173585Yorkshire714914505173585_Yorkshire2024-01-20 11:23:33.9370020{}026LondonNaTFalse9Nature,Food and Drink,History,Arts and Culture{}3.260False00002NaN02024-05-2310False00NaNFalse
53619480618589444East Highlands619480618589444_East Highlands2024-01-20 11:52:41.71400240{}0306452460ChelmsfordNaTFalse3History,Food and Drink,Nature{}33.550False00006NaN02024-03-1610False10NaNFalse
80687603529986566Andalusia687603529986566_Andalusia2024-01-20 12:31:22.9360020{}026LondonNaTFalse7Arts and Culture,History,Food and Drink,Relaxation,Nature{}28.920False00010NaN02024-07-1810False00NaNFalse
86764573606047540Highlands & Islands764573606047540_Highlands & Islands2024-01-20 12:39:01.2360023{}022376242BrightonNaTFalse4Relaxation,Food and Drink{}15.800False00000NaN02024-03-1610False00NaNTrue
87764573606047540East Highlands764573606047540_East Highlands2024-01-20 12:39:01.2360022{}022376242BrightonNaTFalse4Relaxation,Food and Drink{}11.840False00000NaN02024-03-1610False00NaNTrue
88808561449369965Yorkshire808561449369965_Yorkshire2024-01-20 12:40:42.3670020{14}126LondonNaTFalse6Nature,History,Good for kids{}4.280False00006NaN02024-03-2810False00NaNFalse
92727428801083979Yorkshire727428801083979_Yorkshire2024-01-20 12:43:41.4810020{14}126LondonNaTFalse6Nature,History{}5.480False00005NaN02024-03-2810False00NaNFalse
97628563990972298Yorkshire628563990972298_Yorkshire2024-01-20 12:45:58.3070020{14}126LondonNaTFalse6Nature,Good for kids{}2.650False00003NaN02024-03-2810False00NaNFalse
createtripidregioncreatetripid_regionrequestdateaborteddataerrorcountaccommodationunavailableerrorcountcreatetripformsubmission_adultcountbadproportionerrorcountcreatetripformsubmission_childagescreatetripformsubmission_childcountcreatetripformsubmission_closestcity_idcreatetripformsubmission_closestcitycreationdatecreatetripformsubmission_directjourneycreatetripformsubmission_durationdaysarray_to_stringcreatetripformsubmission_extrainfofailtimesecondsfailureindurationserrorcountcreatetripformsubmission_fastjourneyfirstchoiceaccommodationunavailablecountnoavailabledotwaccommodationerrorcountnofamilymanualfallbackserrorcountnogoodscoreerrorcountnorouteserrorcountjotform_onewaytripovernightreductionerrorcountcreatetripformsubmission_preferreddatecreatetripformsubmission_roomcountsubstituteaccommodationunavailablecountsuccesstimeouterrorcounttransportunavailableerrorcounttripbuildtimesecondscreatetripformsubmission_twinroompreferred
4768668413321135879Highlands & Islands668413321135879_Highlands & Islands2024-01-24 18:53:49.2870020{}042771065LiverpoolNaTFalse7Arts and Culture,History,Food and Drink,Nature{}6.440False00001NaN02024-07-1810False00NaNFalse
4786872823512594216East Highlands872823512594216_East Highlands2024-01-24 19:39:49.43500228{}0306452460ChelmsfordNaTFalse3Relaxation,Arts and Culture,Nature{}27.720False000110NaN02024-03-2010False00NaNFalse
4828851628939291587Yorkshire851628939291587_Yorkshire2024-01-24 20:32:53.9350020{}022371813Exeter St David'sNaTFalse4Arts and Culture,Relaxation,History,Food and Drink{}6.000False00001NaN02025-03-0310False00NaNTrue
4863796306217429950East Highlands796306217429950_East Highlands2024-01-24 21:06:30.85600288{}0394536297GuildfordNaTFalse3nan{}69.990False000010NaN02024-10-0110False20NaNFalse
4864796306217429950Highlands & Islands796306217429950_Highlands & Islands2024-01-24 21:06:30.85600224{}0394536297GuildfordNaTFalse3nan{}45.830False00020NaN02024-10-0110False00NaNFalse
4879887844898490614Hauts-de-France887844898490614_Hauts-de-France2024-01-24 21:19:07.3180020{12,12,15}326LondonNaTFalse7Adventure,Good for kids{}142.920False294792010NaN02024-03-293294False10NaNFalse
4880887844898490614Alsace887844898490614_Alsace2024-01-24 21:19:07.3180020{12,12,15}326LondonNaTFalse7Adventure,Good for kids{}6.840False1528019NaN02024-03-29315False00NaNFalse
4881887844898490614French Riviera887844898490614_French Riviera2024-01-24 21:19:07.3180020{12,12,15}326LondonNaTFalse7Adventure,Good for kids{}259.560False17058134136NaN02024-03-293170False10NaNFalse
4933727894810164352Highlands & Islands727894810164352_Highlands & Islands2024-01-24 22:16:45.72000118{15,15}2183936590CardiffNaTFalse3Food and Drink,Relaxation,History,Nature,Adventure{}14.640False110111NaN02024-06-0111False00NaNFalse
4941902485765764024Highlands & Islands902485765764024_Highlands & Islands2024-01-24 22:41:30.1250024{}022376242BrightonNaTFalse5Relaxation,Nature{}23.250False00000NaN02024-03-2010False00NaNFalse